From pioneering research to an unprecedented product

Our story

It all started in 1976 with a curious and talented research student, Tore Risch, who then started focusing on databases and query processing after studies and research within the field of AI.

His supervisor was Prof. Erik Sandewall at the University of Uppsala—a pioneer within Swedish AI research, and who was supervised in turn by the legendary computer scientist John McCarthy who coined the term artificial intelligence in 1956, and developed the Lisp programming language.

‍During this time, Tore had been focusing on conventional databases which handle only one single type of data representation—the persistent table. In 2000, Tore was appointed professor in Database Technology, and got in contact with astrophysicists, in Uppsala and at Astron, the Dutch foundation for astronomy research, who struggled with a different kind of challenge regarding search and analysis of data.


Using radio telescopes, astrophysicists receive huge amounts of data in continuous streams, so large that it is not even possible to store all the data on any medium for a subsequent data analysis. Instead, the search and data analysis had to be made directly on moving data within streams, in real time.

Together with a PhD student and internet industry technical lead, Erik Zeitler, Tore and Erik started to investigate a completely new approach to data analysis and AI.

To be able to search directly on data streams, Erik and Tore started developing a new query language and processing techniques that could be used on any kind of data representations—vectors (series of values), matrices (2-dimensional vectors), tensors (ask a physicist about tensors and you will get a lecture), and more. And they also wanted the query language to contain all kinds of mathematical and search based operators, for any kind of search case.

They soon discovered that scalability became an issue, and that the CPU couldn’t easily cope with the task when the data stream became massive and the queries more complex.

Parallel-processing helped, but more optimizations were difficult to gain between the CPU cores, so they developed a highly efficient distribution process.

An opportunity to measure their progress turned up when they found an academic challenge called the Linear Road Benchmark, aiming at comparing performance characteristics of Stream Data Management Systems.
Linear Road specifies a variable tolling system for a fictional urban highway system where tolls are determined based on changing factors such as congestion and accident proximity, and the task is to handle and analyze data sent out by each vehicle on the highway every 30 seconds.
In 2007, the world record was set at managing the data stream from 1.5 highways. Based on new ideas, they soon managed to build a system that could handle 0.5 highways on a laptop, and a little later 1.5 highways on a PC, which led to some excitement.

With eight cores on a discarded computer cluster, they reached eight highways, and after further advances they managed to handle 64 highways—a result that was published and presented at a conference. However, knowing that this was not the limit, they eventually got the opportunity to demonstrate their methods on a large computer cluster and achieved an astonishing 512 highways (which became a nice conclusion of Erik’s PhD thesis in 2011.)

Fast forward to 2015, and this is where the edge-based strategy of Stream Analyze started to take shape, built around the main software tool, the Stream Analyze Engine, having its roots in the two main unique characteristics of Tore’s and Erik’s research—the capability of searching and analyzing data in large data streams in real time, and building database technology with an extremely small footprint.

Now in 2026, these unique characteristics have delivered a mature product, in market and built on unprecedented and highly advanced technology, but yet easy to use has been a wonderful challenge for our company. But as more experts have joined the company, Stream Analyze can claim a deep and diversified expertise in Edge computing and AI.

Learn more

Take a tour of our platform and explore the use cases, capabilities and components of our Edge AI solution.